Semantic segmentation aims to assign each image pixel a category label. The recent adoption of Convolutional Neural Networks (CNNs) yields various of best-performing methods [1]}, [2]}, [3]} for this task, but the achievement is at the price of a huge amount of dense pixel-level annotations obtained by expensive human labor. An alternative would be resorting to simulated data, such as computer-generated scenes [4]}, [5]}, which can make unlimited amounts of labels available. However, models trained with the simulated images, no matter how perfect they perform on the simulation environment, fail to achieve a same or even similar satisfactory performance on realistic images. The reason behind this performance drop lies in the different data distributions of the two domains, typically known as domain shift [6]}.
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